iEMSs 2016

1. PROFILING RESIDENTIAL WATER USERS’ ROUTINES
BY EIGENBEHAVIOR MODELLING
Andrea Cominola, AndreaMoro, Luca Riva, Matteo Giuliani, AndreaCastelletti

2. RESIDENTIAL URBANWATER MANAGEMENT
. Demand-side management
Long term
water security
Source: United Nations. Department of Economic and Social Affairs. Population Division, 2010
Leflaive, X., et al. (2012), "Water", in OECD, OECD Environmental Outlook to 2050: The Consequences of Inaction, OECD Publishing, Paris.
2000 2030 2050
+130%
Domesticwater
demand
41 megacities
worldwide
Short term
costs
Interventionsduring
low recharge periods

3. RESIDENTIAL URBANWATER MANAGEMENT
. Demand-side management
. Smart metering
1990
1994
50
30
10
1995
1999
2000
2004
2005
2009
2010
2015
134 studies over the last
25 years
Contents lists available at ScienceDirect
Environmental Modelling & Software
journal homepage: www.elsevier.com/locate/envsoft
Environmental Modelling & Software 72 (2015) 198e214
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Contents lists available at ScienceDirect
Environmental Modelling & Software
journal homepage: www.elsevier.com/locate/envsoft
Environmental Modelling & Software 72 (2015) 198e214

4. RESIDENTIAL URBANWATER MANAGEMENT
. Demand-side management
. Smart metering
. Water consumers
DEMAND MANAGEMENT STRATEGIES
. Technological
. financial
. legislative
. operation and maintenance
. education

5. RESIDENTIAL URBANWATER MANAGEMENT
. Demand-side management
. Smart metering
. Water consumers
CUSTOMIZED DEMAND SIDEMANAGEMENT
5% reduction through
customized social norms

6. CHALLENGES
. Smart metered BIG DATA
. How to extract RELEVANTUSERS PROFILES out of large smart
metered datasets?
. How to characterize these profiles, in order to inform
DEMANDMANAGEMENT?

7. CUSTOMER SEGMENTATION
by eigenbehaviormodelling

8. CUSTOMER SEGMENTATION
by eigenbehaviormodelling
Eigenbehavior
extraction

9. CUSTOMER SEGMENTATION
by eigenbehaviormodelling
Eigenbehavior
extraction
Water consumers
clustering
Clusters
characterization

10. CUSTOMER SEGMENTATION
by eigenbehaviormodelling
Eigenbehavior
extraction
Water consumers
clustering
Clusters
characterization

11. 175 water consumers
Hourly resolution water consumption
data
7 monthsmonitoring period
March – November 2015
CASE STUDY
LOCARNO | CH

12. CUSTOMER SEGMENTATION
by eigenbehaviormodelling
Eigenbehavior
extraction
ROUTINE
of each user
HOUSEHOLD WATER
CONSUMPTION
Eagle, Nathan, and Alex Sandy Pentland. "Eigenbehaviors: Identifying structure in routine."
Behavioral Ecology and Sociobiology63.7 (2009): 1057-1066.

13. D(days)
EIGENBEHAVIOR EXTRACTION
SMART METERED
DATA
H (hour of day)

14. EIGENBEHAVIOR EXTRACTION
SMART METERED
DATA
H (hour of day)
DATA
LABELING
D(days)
H (hour of day)
D(days)
Label 0
Label 1
Label 2
Label 3
0.0
0.1
0.2
0.3
0.4
0.5
L0 L1 L2 L3
%ofreadings
LO = 0 L/h
L1 = (0,12] L/h
L2 = (12,100] L/h
L3 > 100 L/h

15. EIGENBEHAVIOR EXTRACTION
SMART METERED
DATA
H (hour of day)
DATA
LABELING
D(days)
H (hour of day)
D(days)
Label 0
Label 1
Label 2
Label 3
BINARY TRANSFORMATION
H (hour of day)
D(days)
H (hour of day) H (hour of day)
Label 1 Label 2 Label 3
H (hour of day)
Label 0

16. EIGENBEHAVIOR EXTRACTION
SMART METERED
DATA
H (hour of day)
DATA
LABELING
D(days)
H (hour of day)
D(days)
Label 0
Label 1
Label 2
Label 3
BINARY TRANSFORMATION
D(days)
PRINCIPAL COMPONENT ANALYSIS
Dimensionality reduction

17. 1st EIGENBEHAVIOR – USER ROUTINE
users
0
23
0
23
0
23
0
23
L0
L1
L2
L3
profile P1 profile P2 profile P3
0.00 0.05 0.10 0.15 0.20 0.25
users
profileP1profileP2profileP3
0.000.050.100.150.200.25
0
0.25
users
0
23
0
23
0
23
0
23
L0
L1
L2
L3
profile P1 profile P2 profile P3
0.00 0.05 0.10 0.15 0.20 0.25
users
0
23
0
23
0
23
0
23
L0
L1
L2
L3
profile P1 profile P2 profile P3
0.00 0.05 0.10 0.15 0.20 0.25
1steigenbehaviorloadings

18. CUSTOMER SEGMENTATION
by eigenbehaviormodelling
Eigenbehavior
extraction
ROUTINE
of each user
HOUSEHOLD WATER
CONSUMPTION
Eagle, Nathan, and Alex Sandy Pentland. "Eigenbehaviors: Identifying structure in routine."
Behavioral Ecology and Sociobiology63.7 (2009): 1057-1066.
Water consumers
clustering
Clusters
characterization
Users community
profiles

19. 1st EIGENBEHAVIOR – USER ROUTINE
users
0
23
0
23
0
23
0
23
L0
L1
L2
L3
profile P1 profile P2 profile P3
0.00 0.05 0.10 0.15 0.20 0.25
users
profileP1profileP2profileP3
0.000.050.100.150.200.25
0
0.25
1steigenbehaviorloadings

20. CHALLENGES
. Smart metered BIG DATA
-> PCA
. How to extract RELEVANTUSERS PROFILES out of large smart
metered datasets?
-> Eigenbehavior extractionand clustering
. How to characterize these profiles, in order to inform
DEMANDMANAGEMENT?
-> Profiles characterization and factor mapping

21. thank you
August 22-25 Monte Verità, Switzerland.
More Info: www2.idsia.ch/cms/smartwater/
www.smarth2o-fp7.eu
@smartH2Oproject
#SmartH2O
@AndreaCominola
@NRMPolimi
Andrea Cominola
andrea.cominola@polimi.it
Politecnico di Milano
Department of Electronics, Information and Bioengineering